def run_simulation(school: SIR, nonschool: SIR):
for _ in range(30):
school.run(1)
nonschool.forward_epi_step(dB = 2*school.dT[-1]//3) # doesn't preserve population
dT_school = np.array(school.dT)
dT_nonschool = np.array(nonschool.dT)
dT = dT_school + dT_nonschool
var_up_school = np.array(school.upper_CI) - dT_school
var_dn_school = np.array(school.lower_CI) - dT_school
var_up_nonschool = np.array(nonschool.upper_CI) - dT_nonschool
var_dn_nonschool = np.array(nonschool.lower_CI) - dT_nonschool
dT_CI_l = dT - np.sqrt(var_dn_school**2 + var_dn_nonschool**2)
dT_CI_u = dT + np.sqrt(var_up_school**2 + var_up_nonschool**2)
return (dT[1:], dT_CI_l[1:], dT_CI_u[1:])
import pymc3 as pm
sir_model = SIR("test",
population=500000,
I0=100,
dT0=20,
Rt0=1.01,
random_seed=0)
total_t = 0
schedule = [(1.01, 75), (1.4, 75), (0.9, 75)]
R0_timeseries = []
for (R0, t) in schedule:
R0_timeseries += [R0] * t
sir_model.Rt0 = R0
sir_model.run(t)
total_t += t
plt.plot(sir_model.dT)
plt.show()
plt.plot(R0_timeseries, "-", color="black", label="$R_0$")
plt.plot(sir_model.Rt, "-", color="dodgerblue", label="$R_t$")
plt.legend(framealpha=1, handlelength=1, loc="best")
plt.PlotDevice().xlabel("time").ylabel("reproductive rate").adjust(left=0.10,
bottom=0.15,
right=0.99,
top=0.99)
plt.ylim(0.5, 1.5)
plt.show()
# 1: parametric scheme:
totals=False)
plt.Rt(dates, Rt_pred[1:], Rt_CI_upper[1:], Rt_CI_lower[1:], CI, ymin=0, ymax=3)\
.title("\nBihar: Reproductive Number Estimate (Covid19India Data)")\
.annotate(f"public data from {str(dates[0]).split()[0]} to {str(dates[-1]).split()[0]}")\
.xlabel("\ndate")\
.ylabel("$R_t$", rotation=0, labelpad=20)\
.show()
Bihar = SIR(name="Bihar",
population=99_000_000,
dT0=T_pred[-1],
Rt0=Rt_pred[-1],
mobility=0,
random_seed=0)
Bihar.run(14)
plt.daily_cases(dates, T_pred[1:], T_CI_upper[1:], T_CI_lower[1:], new_cases_ts[1:], anomaly_dates, anomalies, CI,
prediction_ts = [(Bihar.dT[:-1], Bihar.lower_CI[1:], Bihar.upper_CI[1:], None, "predicted cases")])\
.title("\nBihar: Daily Cases")\
.xlabel("\ndate")\
.ylabel("cases\n")\
.annotate("\nBayesian training process on empirical data, with anomalies identified")\
.show()
# now, do district-level estimation
smoothing = 10
district_time_series = get_time_series(dfn[dfn.detected_state == "Bihar"],
"detected_district").Hospitalized
migration = np.zeros((len(district_names), len(district_names)))
estimates = []